Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging

TL;DR

Synthetic power analyses offer a cost-effective method for estimating statistical power in cognitive neuroimaging, utilizing synthesized brain data to inform sample size decisions without extensive pilot studies.

Contribution

This paper introduces synthetic power analyses as a novel framework for power estimation using generative models, reducing reliance on costly pilot data in neuroimaging studies.

Findings

Synthetic power analysis performs comparably to traditional methods.

The approach is effective when experiments share cognitive processes with previous studies.

Proposed statistical modifications yield conservative and reliable results.

Abstract

In the experimental sciences, statistical power analyses are often used before data collection to determine the required sample size. However, traditional power analyses can be costly when data are difficult or expensive to collect. We propose synthetic power analyses; a framework for estimating statistical power at various sample sizes, and empirically explore the performance of synthetic power analysis for sample size selection in cognitive neuroscience experiments. To this end, brain imaging data is synthesized using an implicit generative model conditioned on observed cognitive processes. Further, we propose a simple procedure to modify the statistical tests which result in conservative statistics. Our empirical results suggest that synthetic power analysis could be a low-cost alternative to pilot data collection when the proposed experiments share cognitive processes with previously conducted experiments.